code: def _expand_transform_param_shift(
      tape: qml.tape.QuantumTape,
      argnum=None,
      shifts=None,
      gradient_recipes=None,
      fallback_fn=finite_diff,
      f0=None,
      broadcast=False,
  ) -> (Sequence[qml.tape.QuantumTape], Callable):
      """Expand function to be applied before parameter shift."""
      expanded_tape = expand_invalid_trainable(tape)
  
      def null_postprocessing(results):
          """A postprocesing function returned by a transform that only converts the batch of results
          into a result for a single ``QuantumTape``.
          """
          return results[0]
  
      return [expanded_tape], null_postprocessing
  
imports: from typing import Sequence, Callable
  from functools import partial
  import numpy
  import pennylane
  from pennylane.measurements import VarianceMP
  from pennylane import transform
  from pennylane.transforms.tape_expand import expand_invalid_trainable
  from pennylane.gradients.gradient_transform import _contract_qjac_with_cjac
  from .finite_difference import finite_diff
  from .general_shift_rules import _iterate_shift_rule, frequencies_to_period, generate_shifted_tapes, process_shifts
  from .gradient_transform import _all_zero_grad, assert_no_state_returns, assert_no_tape_batching, assert_multimeasure_not_broadcasted, choose_trainable_params, find_and_validate_gradient_methods, _no_trainable_grad, reorder_grads
label_desc: Use image node_id 17 for calling a global function with example usage: _expand_transform_param_shift(tape, argnum, shifts, gradient_recipes, fallback_fn, f0, broadcast) and returns: list,  null_postprocessing, results